Xiaogang Jiang

Immobilized Zirconium Ion Affinity Chromatography for Specific Enrichment of Phosphopeptides in Phosphoproteome Analysis

Large scale characterization of phosphoproteins requires highly specific methods for purification of phosphopeptides because of the low abundance of phosphoproteins and substoichiometry of phosphorylation. Enrichment of phosphopeptides from complex peptide mixtures by IMAC is a popular way to perform phosphoproteome analysis. However, conventional IMAC adsorbents with iminodiacetic acid as the chelating group to immobilize Fe3+ lack enough specificity for efficient phosphoproteome analysis. Here we report a novel IMAC adsorbent through Zr4+ chelation to the phosphonate-modified poly(glycidyl methacrylate-co-ethylene dimethacrylate) polymer beads. The high specificity of Zr4+-IMAC adsorbent was demonstrated by effectively enriching phosphopeptides from the digest mixture of phosphoprotein (α- or β-casein) and bovine serum albumin with molar ratio at 1:100. Zr4+-IMAC adsorbent was also successfully applied for the analysis of mouse liver phosphoproteome, resulting in the identification of 153 phosphopeptides (163 phosphorylation sites) from 133 proteins in mouse liver lysate. Significantly more phosphopeptides were identified than by the conventional Fe3+-IMAC approach, indicating the excellent performance of the Zr4+-IMAC approach. The high specificity of Zr4+-IMAC adsorbent was found to mainly result from the strong interaction between chelating Zr4+ and phosphate group on phosphopeptides. Enrichment of phosphopeptides by Zr4+-IMAC provides a powerful approach for large scale phosphoproteome analysis.

Octadecylated Silica Monolith Capillary Column with Integrated Nanoelectrospray Ionization Emitter for Highly Efficient Proteome Analysis

An improved strategy for the preparation of octadecylated silica monolith capillary column with high homogeneity was proposed. Column performance was evaluated by nanoscale HPLC. The design for constructing an integrated nanoelectrospray emitter on the octadecylated silica monolith capillary column was first introduced. In comparison with the separated configuration where the emitter is connected to monolithic capillary column by the aid of a zero dead volume union, the integrated capillary column has the inherent advantage of the minimized extracolumn volume thus providing improved separation quality. The performance of the integrated monolithic capillary column was evaluated by separation of BSA tryptic digest, and peak capacity of 313 with a 30-cm column was obtained. The high separation performance allowed highly confident identification of 662 distinct proteins through assignment of 1933 unique peptides by analysis of tryptic digest of 0.5 μg of Saccharomyces cerevisiae proteins. The higher separation efficiency by a 60-cm monolithic capillary column increased the proteome coverage with identification of 1323 proteins through assignment of 5501 unique peptides over 400-min gradient elution.

Technologies and methods for sample pretreatment in efficient proteome and peptidome analysis

Although great progresses have been made in proteomics during the last decade, proteomics is still in its infancy. Extreme complexity of proteome sample and large dynamic range of protein abundance overwhelm the capability of all currently available analytical platforms. Sample pretreatment is a good approach to reduce the complexity of proteome sample and decrease the dynamic range. In this article, we present an overview of different technologies and methods for sample pretreatment in efficient proteome and peptidome analysis. Methods for isolation of rare amino acid‐containing peptides, terminal peptides, PTM peptides and endogenous peptides are reviewed. In addition, two automated sample pretreatment technologies, i.e. automated sample injection and on‐line digestion, are also covered.

Automatic Validation of Phosphopeptide Identifications by the MS2/MS3 Target-Decoy Search Strategy

Manual checking is commonly employed to validate the phosphopeptide identifications from database searching of tandem mass spectra. It is very time-consuming and labor intensive as the number of phosphopeptide identifications increases greatly. In this study, a simple automatic validation approach was developed for phosphopeptide identification by combining consecutive stage mass spectrometry data and the target-decoy database searching strategy. Only phosphopeptides identified from both MS2 and its corresponding MS3 were accepted for further filtering, which greatly improved the reliability in phosphopeptide identification. Before database searching, the spectra were validated for charge state and neutral loss peak intensity, and then the invalid MS2/MS3 spectra were removed, which greatly reduced the database searching time. It was found that the sensitivity was significantly improved in MS2/MS3 strategy as the number of identified phosphopeptides was 2.5 times that obtained by the conventional filter-based MS2 approach. Because of the use of the target-decoy database, the false-discovery rate (FDR) of the identified phosphopeptides could be easily determined, and it was demonstrated that the determined FDR can precisely reflect the actual FDR without any manual validation stage.

Automation of nanoflow liquid chromatography-tandem mass spectrometry for proteome analysis by using a strong cation exchange trap column.

An approach was developed to automate sample introduction for nanoflow LC‐MS/MS (μLC‐MS/MS) analysis using a strong cation exchange (SCX) trap column. The system consisted of a 100 μm id×2 cm SCX trap column and a 75 μm id×12 cm C18 RP analytical column. During the sample loading step, the flow passing through the SCX trap column was directed to waste for loading a large volume of sample at high flow rate. Then the peptides bound on the SCX trap column were eluted onto the RP analytical column by a high salt buffer followed by RP chromatographic separation of the peptides at nanoliter flow rate. It was observed that higher performance of separation could be achieved with the system using SCX trap column than with the system using C18 trap column. The high proteomic coverage using this approach was demonstrated in the analysis of tryptic digest of BSA and yeast cell lysate. In addition, this system was also applied to two‐dimensional separation of tryptic digest of human hepatocellular carcinoma cell line SMMC‐7721 for large scale proteome analysis. This system was fully automated and required minimum changes on current μLC‐MS/MS system. This system represented a promising platform for routine proteome analysis.

Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics.

BackgroundIn proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, ΔCn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now.ResultsIn this study, we implemented a machine learning approach known as predictive genetic algorithm (GA) for the optimization of filtering criteria to maximize the number of identified peptides at fixed false-discovery rate (FDR) for SEQUEST database searching. As the FDR was directly determined by decoy database search scheme, the GA based optimization approach did not require any pre-knowledge on the characteristics of the data set, which represented significant advantages over statistical approaches such as PeptideProphet. Compared with PeptideProphet, the GA based approach can achieve similar performance in distinguishing true from false assignment with only 1/10 of the processing time. Moreover, the GA based approach can be easily extended to process other database search results as it did not rely on any assumption on the data.ConclusionOur results indicated that filtering criteria should be optimized individually for different samples. The new developed software using GA provides a convenient and fast way to create tailored optimal criteria for different proteome samples to improve proteome coverage.

Automation of nanoflow liquid chromatography-tandem mass spectrometry for proteome and peptide profiling analysis by using a monolithic analytical capillary column

An automated nano‐LC‐MS/MS platform without trap column was established, which only used a 20 cm lauryl methacrylate–ethylene dimethacrylate (LMA–EDMA) monolithic capillary column to allow preconcentration and separation of peptides. The monolithic column had the advantages of good permeability and low backpressure resulting in higher flow rates for capillary columns. Tryptic digests of bovine albumin and yeast protein extract were tested using the monolithic column system. High proteomic coverage using this approach were demonstrated in this study. Furthermore, peptide samples extracted from mouse liver were separated by using the monolithic column system combined with size‐exclusion chromatography prefractionation. This monolithic column system might be a promising alternative for the automated system previously using a trap column for routine proteome and peptide profiling analysis.